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95.223.221.107: heading formatting for definition and explanation

2014-02-18T10:13:43Z

heading formatting for definition and explanation

← Older revision		Revision as of 12:13, 18 February 2014
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	~~'''Term Discrimination''' is a way to rank keywords in how useful they are for [[Information Retrieval]].~~

	~~== Overview ==~~

	~~This is a method similar to [[tf~~-~~idf]] but it deals with finding keywords suitable for [[information retrieval]] and ones~~ that ~~are not.~~ ~~Please refer to [[Vector Space Model]] first~~.		Surely the second option would be more beneficial for any website. Medical word press themes give you the latest medical designs. The Word - Press Dashboard : an administrative management tool that supports FTP content upload 2. Word - Press also provides protection against spamming, as security is a measure issue. After activating, you will find their website link and get the activation code from their website. <br><br>Word - Press is known as the most popular blogging platform all over the web and is used by millions of blog enthusiasts worldwide. You will have to invest some money into tuning up your own blog but, if done wisely, your investment will pay off in the long run. Some plugins ask users to match pictures or add numbers, and although effective, they appear unprofessional and unnecessary. So if you want to create blogs or have a website for your business or for personal reasons, you can take advantage of free Word - Press installation to get started. As soon as you start developing your Word - Press MLM website you'll see how straightforward and simple it is to create an online presence for you and the products and services you offer. <br><br>You can down load it here at this link: and utilize your FTP software program to upload it to your Word - Press Plugin folder. Word - Press has ensured the users of this open source blogging platform do not have to troubleshoot on their own, or seek outside help. Setting Up Your Business Online Using Free Wordpress Websites. User friendly features and flexibility that Word - Press has to offer is second to none. There are plenty of tables that are attached to this particular database. <br><br>Word - Press has plenty of SEO benefits over Joomla and Drupal. And, that is all the opposition events with nationalistic agenda in favor of the individuals of Pakistan marching collectively in the battle in opposition to radicalism. The templates are designed to be stand alone pages that have a different look and feel from the rest of your website. Word - Press is the most popular open source content management system (CMS) in the world today. This includes enriching the content with proper key words, tactfully defining the tags and URL. <br><br>Here's more info in regards to [http://bikeracinggames.co.in/profile/omb83.html wordpress backup plugin] visit our webpage. More it extends numerous opportunities where your firm is at comfort and rest assured of no risks & errors. Being a Plugin Developer, it is important for you to know that development of Word - Press driven website should be done only when you enable debugging. As a result, it is really crucial to just take aid of some experience when searching for superior quality totally free Word - Press themes, Word - Press Premium Themes for your web site. If this is not possible you still have the choice of the default theme that is Word - Press 3. Definitely when you wake up from the slumber, you can be sure that you will be lagging behind and getting on track would be a tall order.

	~~This method uses the concept of ''Vector Space Density'' that the less dense an [[occurrence matrix]]~~ is, ~~the better an information retrieval query~~ will ~~be.~~

	~~An optimal index term is one that can distinguish two different documents from each other~~ and ~~relate two similar documents. On~~ the ~~other hand, a sub-optimal index term can not distinguish two different document~~ from ~~two similar documents~~.

	~~The discrimination value is the difference in the occurrence matrix's vector-space density versus the same matrix's vector-space without the index term's density.~~

	~~Let:~~
	<~~math~~>A<~~/math~~> be the ~~occurrence matrix~~
	~~<math>A_k</math> be~~ the ~~occurrence matrix without~~ the ~~index term <math>k</math>~~
	and ~~<math>Q(A)</math> be density~~ of ~~<math>A</math>~~.
	~~Then:~~
	~~The discrimination value of the index term <math>k</math>~~ is:
	~~<math>DV_k = Q(A) - Q(A_k)</math>~~

	~~== How~~ to ~~compute ==~~

	~~Given~~ an ~~[[occurrency matrix]]: <math>A</math>~~ and ~~one keyword: <math>k</math>~~
	* Find the ~~global document [[centroid]]:~~ <~~math~~>C<~~/math~~> (this ~~is just~~ the ~~average document vector)~~
	* Find the average [[euclidean distance]] from every document vector, ~~<math>D_i</math>~~ to <~~math~~>C<~~/math~~>
	* Find the average euclidean distance from every document vector, ~~<math>D_i</math> to <math>C</math> ''IGNORING'' <math>k</math>~~
	* The difference between the ~~two values~~ in the ~~above step is the ''discrimination value'' for keyword <math>K</math>~~

	~~A higher value is better because including~~ the ~~keyword will result~~ in ~~better information retrieval~~.

	~~== Qualitative Observations ==~~
	~~Keywords that~~ are ~~''[[Sparse matrix\|sparse]]'' should~~ be ~~poor discriminators because they have poor ''[[Precision and recall\|recall]],''~~
	~~whereas~~
	~~keywords~~ that ~~are ''frequent'' should be poor discriminators because they~~ have ~~poor ''[[Precision~~ and ~~recall\|precision]]~~.''

	~~== References ==~~
	* [[Gerard Salton\|G. ~~Salton]], A. Wong~~, and C. ~~S. Yang (1975), "~~[http://~~www~~.cs.~~uiuc.edu/class/fa05/cs511~~/~~Spring05~~/~~other_papers/p613-salton~~.~~pdf A Vector Space Model for Automatic Indexing~~],~~" ''Communications~~ of ~~the ACM'', vol~~. 18, ~~nr. 11~~, ~~pages 613–620~~. ~~''(The article in which~~ the ~~vector space model was first presented)''~~

	* Can, F., Ozkarahan, E. A (1987), "Computation of term/document discrimination values by use of the ~~cover coefficient concept~~.~~" ''Journal of~~ the ~~American Society for Information Science''~~, ~~vol. 38, nr. 3, pages 171-183~~.

	~~[[Category:Information retrieval]]~~

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2013-11-17T11:42:09Z

External links

← Older revision		Revision as of 13:42, 17 November 2013
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	I'm a ~~50 years old~~ and ~~study at the university (Education Science)~~.~~<br>In my free time I learn Korean~~. I'~~ve been there~~ and ~~look forward to go there anytime soon~~. ~~I like to read~~, ~~preferably on my ipad~~. ~~I like to watch~~ The ~~Big Bang Theory~~ and ~~Arrested Development as well as documentaries about nature~~. ~~I enjoy Radio~~-~~Controlled Car Racing.~~<br><br>~~my blog~~ :: [http://~~bikeracinggames~~.co.in/~~profile/omb83~~.~~html wordpress backup plugin~~]		'''Term Discrimination''' is a way to rank keywords in how useful they are for [[Information Retrieval]].

			== Overview ==

			This is a method similar to [[tf-idf]] but it deals with finding keywords suitable for [[information retrieval]] and ones that are not. Please refer to [[Vector Space Model]] first.

			This method uses the concept of ''Vector Space Density'' that the less dense an [[occurrence matrix]] is, the better an information retrieval query will be.

			An optimal index term is one that can distinguish two different documents from each other and relate two similar documents. On the other hand, a sub-optimal index term can not distinguish two different document from two similar documents.

			The discrimination value is the difference in the occurrence matrix's vector-space density versus the same matrix's vector-space without the index term's density.

			Let:
			<math>A</math> be the occurrence matrix
			<math>A_k</math> be the occurrence matrix without the index term <math>k</math>
			and <math>Q(A)</math> be density of <math>A</math>.
			Then:
			The discrimination value of the index term <math>k</math> is:
			<math>DV_k = Q(A) - Q(A_k)</math>

			== How to compute ==

			Given an [[occurrency matrix]]: <math>A</math> and one keyword: <math>k</math>
			* Find the global document [[centroid]]: <math>C</math> (this is just the average document vector)
			* Find the average [[euclidean distance]] from every document vector, <math>D_i</math> to <math>C</math>
			* Find the average euclidean distance from every document vector, <math>D_i</math> to <math>C</math> ''IGNORING'' <math>k</math>
			* The difference between the two values in the above step is the ''discrimination value'' for keyword <math>K</math>

			A higher value is better because including the keyword will result in better information retrieval.

			== Qualitative Observations ==
			Keywords that are ''[[Sparse matrix\|sparse]]'' should be poor discriminators because they have poor ''[[Precision and recall\|recall]],''
			whereas
			keywords that are ''frequent'' should be poor discriminators because they have poor ''[[Precision and recall\|precision]].''

			== References ==
			* [[Gerard Salton\|G. Salton]], A. Wong, and C. S. Yang (1975), "[http://www.cs.uiuc.edu/class/fa05/cs511/Spring05/other_papers/p613-salton.pdf A Vector Space Model for Automatic Indexing]," ''Communications of the ACM'', vol. 18, nr. 11, pages 613–620. ''(The article in which the vector space model was first presented)''

			* Can, F., Ozkarahan, E. A (1987), "Computation of term/document discrimination values by use of the cover coefficient concept." ''Journal of the American Society for Information Science'', vol. 38, nr. 3, pages 171-183.

			[[Category:Information retrieval]]

59.144.19.130: /* See also */

2011-11-11T11:37:24Z